PAPER PLAINE

Understanding which firms are truly central to the economy helps policymakers predict how disruptions—like supply chain shocks or bankruptcies—will ripple through the system. Companies in concentrated supplier positions become critical pressure points; losing them hurts more firms downstream. This map also reveals why some regions and industries are more economically fragile than others, information that's essential for designing targeted interventions during crises.

DeFi lending now handles tens of billions in real money, and depositors often don't understand the risks they're taking. This framework gives regulators and platforms a concrete way to spot trouble before it happens—similar to how banks monitor their capital reserves. As DeFi shifts toward using actual dollars instead of just cryptocurrency collateral, having a reliable risk-measurement system becomes essential to prevent another crypto collapse that could wipe out ordinary investors.

The finding gives policymakers a concrete budget figure for what ending extreme poverty would actually require, making it easier to evaluate whether current aid spending is adequate or whether ending poverty is a genuinely affordable goal. At less than one-third of one percent of global GDP, the cost is small enough that the obstacle to ending poverty is primarily political will rather than economic impossibility.

As companies deploy large language models as autonomous economic agents—in trading, bidding systems, and marketplace negotiations—these results show that markets designed for human behavior may malfunction with AI. The inefficient allocations mean potential losses for buyers and sellers, and the unpredictability across different AI models creates risk for anyone building systems that mix human and AI traders.

Social media platforms, public health campaigns, and online marketplaces all operate in networked settings where one person's treatment ripples to others. Current methods for estimating causal effects assume people are isolated — a false assumption that leads to wrong answers. This work enables experimenters to reliably estimate what new policies would accomplish, and to quantify their uncertainty when the network structure is only partially understood.

Understanding how people behave under ambiguous uncertainty matters for everything from financial regulation to insurance pricing to public health policy, where real decisions often involve incomplete information. This dataset is large and detailed enough that researchers can now test competing theories about ambiguity-aversion side by side, and replicate findings that might otherwise stay trapped in individual labs.

Companies spending millions on AI training for their workforce may be wasting money if those programs don't address how employees' actual domain knowledge shapes their AI use. The findings suggest managers should focus on matching AI tools to senior experts and strategic teams rather than rolling out one-size-fits-all training—and that simply teaching people to use AI tools matters far less than what they already know about their work.

Online platforms and social networks amplify the voices of confident speakers, often regardless of accuracy. This work predicts exactly when that amplification helps versus harms collective understanding—and suggests that limiting how widely confident wrong ideas travel could prevent communities from locking into large-scale false consensus.

Price data feeds into inflation estimates, wage negotiations, and policy decisions across economics and business. If researchers systematically underestimate the true range of uncertainty in their findings, they may report confident conclusions that are actually fragile. The paper's results suggest that better data design—ensuring prices move independently across different regions or products rather than moving together—could roughly double the reliability of these estimates.

As AI becomes embedded in workplaces, managers and organizations need to understand that the same tool behaves differently depending on how people access it. Work contexts naturally push people toward specifying tasks clearly before execution, while chat interfaces encourage tinkering and feedback loops. This shapes what traces of human control remain in AI decisions — a critical issue for accountability, audits, and knowing who is really responsible when something goes wrong.

The EU has staked its global climate credibility on the 55% reduction target, but hitting it now requires far more aggressive intervention than current policies deliver. Transportation's structural slowness—affecting countries across the bloc—means the problem can't be solved by a few policy fixes; Europe needs wholesale changes to how people and goods move, not just incremental tweaks to existing measures.

Standard financial models assume people experience time the same way regardless of what they're deciding. This research shows the brain actually stretches or compresses financial time based on valuation activity, which could explain why investors make inconsistent choices between mathematically equivalent options and why the same delay feels different in different emotional states.

Researchers use life-satisfaction inequality as a way to measure whether societies are truly unequal in how well people live. If the metric is partly contaminated by how people round their answers rather than genuine differences in wellbeing, policy makers and economists could be acting on incomplete information—though this study shows the damage may be less severe than feared, since the distortion affects all countries roughly the same way.

Current climate proposals focus on getting the best economic outcome per ton of carbon reduced. This research shows that's backwards: a less efficient deal that members stick with will outperform a perfectly designed deal that collapses when a government changes or a country breaks its promises. The analysis maps three real proposals—carbon currencies, border adjustments, and export rebates—to show which ones can ignite on their own and which will fail without constant diplomatic maintenance.

Policy decisions about job training, tax credits, or health programs often rest on estimates from observational data where the counterfactual is guessed. Traditional methods claim narrow confidence intervals but deliver false certainty—the coverage is nearly zero when data is sparse. This method trades some precision for honesty: its stated margins actually contain the true answer, making it safer for policymakers to rely on.

Airport congestion wastes money and passenger time—savings of hundreds of millions dollars could be reinvested in service improvements or passed to travelers. Fewer delayed flights also mean less fuel burned and lower emissions from planes sitting on tarmacs. These economic benefits provide a concrete case for high-speed rail investment beyond the usual environmental and commute-time arguments.

Uncontrolled AI drift in asset management could concentrate risk across the financial system without regulators or firms noticing until it's too late. The framework and 90-day implementation roadmap give institutions concrete tools to govern AI trading before it becomes a systemic failure point, similar to how correlated human fund managers amplified past market crashes—but faster and less visible.

This explains a puzzling gap between how economic theory says people should invest and how they actually do near financial cliffs. The finding suggests that apparent risk aversion in real portfolios might be rational responses to real constraints rather than personality quirks—which could improve how we model everything from personal retirement planning to corporate risk management.

Companies waste millions funding AI projects that look good on paper but fail in practice. By separating value, likelihood, and cost into independent judgments, teams can make smarter bets earlier—and avoid costly failures. The framework works even with rough estimates rather than precise numbers, making it practical for any organization deciding where to invest in AI.

Inequality measurements guide policy decisions on taxation, welfare, and development aid, so how we measure it shapes real outcomes for millions of people. This new method offers a mathematically cleaner alternative that could challenge current rankings of which countries are most unequal, potentially shifting where international attention and resources focus. The approach is also practical: its simplest version has a closed-form solution that makes calculations straightforward rather than computationally intensive.

Russia's weaponization of gas supplies has exposed Europe's strategic weakness. This research shows that energy independence is actually affordable, not a luxury. But it also reveals a trap: without additional safeguards, Europeans could still face price shocks from global markets even after becoming self-sufficient, meaning policy must go beyond just building alternatives to actually shield consumers from volatility.

Portfolio managers and AI systems that allocate resources can now use these mathematical rules to systematically reduce regret and improve returns without needing perfect information. The framework applies directly to investment tilting strategies and large language models choosing how to distribute computational resources, making it practical for anyone optimizing decisions under uncertainty.

Efficiency analysis affects major decisions: regulators use it to assess utility companies and hospitals, investors use it to value firms, and governments use it to benchmark public agencies. Using the wrong statistical assumptions can lead to systematically incorrect efficiency rankings. This work shows researchers can now check whether their choice of statistical model is driving their conclusions, rather than having those conclusions rest on an untested assumption about how errors behave.

As research institutions consider using AI to supplement or replace peer review, this suggests that more complex AI methods don't automatically produce more useful critique. The result is a cautionary finding for anyone designing AI feedback systems: computational sophistication alone doesn't guarantee better quality, and the people whose work is being evaluated remain the most reliable judges of what actually helps them improve their research.

Trade negotiations and sanctions are built on assumptions about mutual vulnerability, but this research shows the leverage is wildly unequal. A country's actual bargaining power in trade disputes depends on how easily it can switch suppliers or find new buyers—something determined by its position in the global economy, not the size of its bilateral trade deficit. Policymakers threatening sanctions or trade restrictions often overestimate their leverage and underestimate the costs to their own economies if a partner retaliates by finding alternatives.

Policy makers rely on accurate impact estimates to decide whether programs are worth the cost. If researchers measure spillovers using the wrong distance or formula, they systematically underestimate or overestimate program effects. This framework provides a testable, data-driven way to get the measurement right, which directly changes which policies look effective and how much funding they deserve.

Countries currently use GDP to guide trillion-dollar decisions on spending, regulation, and priority-setting—even though it ignores health, inequality, and environmental damage. A better wellbeing index could redirect policy toward what actually improves lives. The paper shows that how you build this index matters enormously: different methods rank countries in completely different orders, meaning the choice of measurement method itself becomes a political decision that affects which nations look successful and which look like they're failing.

Brazil's electricity system relies heavily on hydropower, and biased forecasts cascade from planning decisions into actual market prices and grid reliability. The researchers show these distortions are not just statistical mistakes but structural problems that push the entire system toward inefficiency and instability. The same mechanism likely affects other countries with large hydroelectric systems—meaning fixing forecast accuracy could reduce electricity costs and improve reliability across multiple continents.

When companies only count money, they systematically ignore pollution, broken communities, and eroded trust — costs that everyone else pays. A standard social balance sheet would make harm visible to investors, regulators, and the public, giving markets real information for the first time. This could shift which companies attract capital and which face pressure to change, making social responsibility a business requirement rather than an optional add-on.

As companies and researchers increasingly use LLMs to simulate human behavior in economic or social experiments, this work reveals a critical flaw: matching surface-level choice distributions isn't enough. An AI might pick the same numbers as humans, but for entirely different reasons—which means using LLMs as stand-ins for human subjects in behavioral research could lead to false conclusions about how people actually make decisions under pressure.

Transportation agencies, retailers, and economists use choice models to estimate how people respond to prices and policies. An accurate model that also obeys economic logic is more trustworthy for real decisions—whether predicting traffic patterns after a toll increase or estimating consumer welfare. This method lets organizations use faster, more accurate foundation models without sacrificing the economic guarantees that justify policy reliance on their outputs.

When market participants strategically misreport demand, it distorts electricity prices, wastes renewable energy, and destabilizes the grid. This mechanism makes the market work as intended without requiring operators to monitor every participant — honest traders face no penalty, while cheaters get priced out.

Today's major platforms (Facebook, TikTok, YouTube) legally own or control the content creators produce, giving them power over what gets shown, removed, or monetized. Cryptographically-signed content that creators control themselves could shift that power back: creators would own their work outright, decide how it spreads, and keep the benefits. This matters for anyone who posts, writes, or creates online and wants genuine ownership of what they make.

Countries and companies are pouring billions into AI tools that sit underutilized because workers lack the cognitive skills to integrate them into their jobs. South Korea exemplifies the problem: despite strong workforce education and significant AI investment, low convergence capacity means minimal actual productivity gain. The framework suggests that before buying more AI, organizations need to invest in training that builds workers' ability to learn across domains, think flexibly, and adapt—a shift that could unlock trillions in stranded AI value currently going unrealized.

European governments and grid operators make billion-euro decisions about energy policy, transmission upgrades, and emergency reserves based on price forecasts. Understanding which factors actually move prices—rather than just predicting them—lets policymakers target the right levers: they might invest differently in solar storage if solar truly dominates price swings, or prioritize grid upgrades between countries if interconnections reshape regional economics. This analysis also shows what a genuinely unified European market would look like, crucial information as the EU pushes toward deeper energy integration.

Policymakers trying to accelerate the shift to renewable energy often assume the main barriers are technological or financial. This research suggests inequality itself is the lock. It implies that climate plans which ignore wealth distribution—taxing the rich heavily without redistributing gains—will fail or move glacially. Countries may need to combine green investment with income redistribution, not choose between them.

As universities and companies race to use AI for research, blindly trusting AI outputs can publish false findings that waste resources and mislead policy. This framework shows that reliability doesn't require better AI alone—it requires better workflow design, with specific checkpoints where human judgment stops bad analyses before they reach publication. The method is practical enough to deploy today with existing tools.

Companies face mounting pressure from regulations like the EU's carbon rules and tariffs on high-emission goods, but most lack a clear playbook for pursuing both goals simultaneously. This research gives business leaders a concrete framework to reorganize their operations and supply chains to meet both demands, while showing which regions and talent pools are best positioned to support this shift. That means faster paths to compliance, lower environmental costs, and new competitive advantages for early movers.

Criminal investigators and regulators trying to track cryptocurrency movements hit blind spots when they only look at transactions. Understanding the full lifecycle—including where data lives off-chain and how privacy tools obscure records—reveals where enforcement actually works and where gaps exist. This framework also helps policymakers design smarter regulations targeting specific lifecycle stages rather than treating all blockchain activity the same way.

Software development is geographically concentrated in ways that matter for economic opportunity: if you're a developer outside major tech hubs, the skills you can learn—and the timing you learn them—depends on your city's existing specialization. Understanding these patterns could help policymakers and companies identify where emerging technologies will take hold and which regions risk falling behind as new skills become essential.

This finding reshapes how economists think about population cycles and intergenerational fairness. Rather than treating fertility and economic growth as purely technical problems, it shows that the size of your birth cohort directly determines your lifetime welfare — a hard constraint that policy cannot easily escape through savings rates or capital investment alone.

Match-fixing threatens the credibility of sports and costs leagues millions in lost revenue and fan trust. Football betting markets handle more money globally than any other sport, making them a prime target for manipulation. A tool that automatically flags suspicious betting patterns could help sports authorities catch cheating before it spreads, protecting the integrity of competitions that billions of fans rely on.

Renewable energy requires massive upfront investment, and lenders demand stable cash flows before they'll finance a project. Right now, many countries use expensive subsidy contracts to provide that stability. This research shows cheaper contract designs could deliver the same financial security while letting wind farms respond to real electricity market conditions, potentially lowering the overall cost of clean energy and reducing hidden subsidies.

As companies consider deploying LLMs for economic decisions and negotiations, this shows current AI systems lack the flexible strategic thinking humans naturally apply. LLMs produced predictable, exploitable strategies that humans quickly learned to beat. The finding suggests humans and AI shouldn't yet be considered interchangeable for high-stakes competitive situations where adaptability matters—and that careful human judgment remains essential in strategic settings.

Governments use lifetime earnings predictions to design pension systems, tax policy, and welfare programs. Current methods miss real patterns in how earnings actually change over time, leading to inaccurate estimates of inequality and insufficient planning for retirement security. SAGA's 31–38 percent improvement in accuracy could help policymakers better anticipate future costs and design fairer systems—and the researchers released their model publicly so other governments can test it on their own data.

Economic policymakers and investors make decisions based on how markets will evolve over time. This model explains real-world patterns in firm formation, job flows, and income distribution by showing that entry-exit dynamics have predictable structure and can be optimized. Companies and governments can use these insights to design policies that steer markets toward desired outcomes rather than treating entry and exit as uncontrollable forces.

Treasury bonds underpin the U.S. financial system, influencing everything from mortgage rates to pension valuations. Better models of when bond market behavior fundamentally shifts would help investors, central banks, and policymakers anticipate dangerous transitions—like shifts toward persistent volatility—rather than getting caught off guard. However, this paper's finding that transition timing mechanisms are nearly impossible to pin down statistically suggests that even sophisticated models may give false confidence in predicting exactly when the next regime change will strike.

Auction designers and platforms lose money when bidders don't understand how to participate effectively. If regulators or companies can identify which auctions are strategically similar, they can reuse the same educational materials and training across different markets instead of starting from scratch each time. This could reduce the time and cost needed to onboard bidders to new auction formats.

As prediction markets grow and add leverage features, platforms and regulators need to know which manipulation tactics actually work and which safeguards backfire. Without this roadmap, leverage could shift cheating from hard-to-detect price games to outcome manipulation that distorts real elections, financial forecasts, or sporting events—while traders park their money in whichever country's rules make cheating easiest.

As AI tools become standard for policy analysis, business forecasting, and academic research, faulty causal claims now spread with unprecedented speed and polish, making their errors harder to catch. When a formatted spreadsheet or polished chart is your only signal of validity, and recognizing problems requires expertise the AI workflow sidesteps, bad analysis can drive real decisions—from business strategy to public policy—before anyone spots the mistake. The proposed Analysis Contract creates an audit trail that forces rigor back into the process.

NYC residents in low-income and communities of color have historically waited longer for building inspections and housing repairs. This AI system could cut those wait times by routing complaints to the right teams faster, meaning families get heat in winter or safe scaffolding fixed sooner. The approach also shows that fair service doesn't mean treating everyone identically—it means understanding which neighborhoods have persistent problems that need priority attention.

This finding challenges the premise that venture capitalists earn their 2-and-20 fees through superior judgment. If VC performance is indistinguishable from random allocation, it raises hard questions about whether investors should pay premium fees for what amounts to passive exposure to startups. The same pattern holds for stock analysts picking companies, suggesting skill is difficult to prove in any extreme winner-take-most market.

Prediction markets are used to forecast election outcomes, pandemic severity, and tech breakthroughs — decisions that depend on whether price movements mean something real. If traders or manipulators can make prices move without providing genuine information, the market becomes less reliable for forecasting. This index makes it possible to flag when a price move might be noise or manipulation rather than actual wisdom.

Norway's electricity traders, grid operators, and energy companies rely on accurate price forecasts to make buying and selling decisions worth millions of euros daily. The old models trained on pre-crisis data were giving them false confidence in their predictions. This research provides updated benchmarks that work across all five zones, and shows traders which models and feature combinations to trust—and critically, when those models are likely to fail. The finding that simpler models work just as well in routine conditions could save companies from overcomplicating their systems, while the warning about stressed regimes gives decision makers a concrete signal for when to add extra caution to their bets.

Policymakers trying to firewall their economies from global financial shocks need to know which transmission routes matter most in each type of crisis. Trade restrictions might help in some scenarios but miss the real danger in others. This framework reveals which channel to target, potentially saving governments from deploying expensive or ineffective crisis responses. The method also surfaces when the evidence is genuinely uncertain—transparency the researchers say is missing from most contagion research.

States and cities compete fiercely to attract top talent through tax breaks and subsidies, betting that star inventors will boost local innovation. This research shows those bets are grounded in real effects — but also reveals a hidden cost: tax-driven clustering means inventive activity ends up in the wrong places, leaving other regions less innovative than they'd naturally be. Understanding these spillovers could help policymakers design smarter incentives that benefit entire regions rather than just chasing individual winners.

This reshapes how we think about monetary policy and economic stability. It suggests that tying currency to gold doesn't eliminate inflationary pressure—it redirects it toward savers and creditors at the expense of borrowers and workers. During deflationary periods, farms and businesses carrying fixed debts face mounting real obligations even as revenues shrink, which may explain why the 1873–1896 era sparked widespread farmer unrest and political upheaval despite falling prices.